Name
Title | ||
|---|---|---|
DGTL-Net: A Deep Generative Transfer Learning Network for Fault Diagnostics on New Hard Disks |
Abstract | ||
|---|---|---|
•Generalization of diagnosis model is improved on new hard disks without faults.•New faulty samples of hard disks could be generated from healthy samples.•Distribution discrepancy between different types of hard disks could be decreased.•End-end EM-based training strategy guarantees a good accuracy and convergence. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2020.114379 | Expert Systems with Applications |
Keywords | DocType | Volume |
AIOps,Industrial applications,Hard disks,Fault diagnostics,Deep generative network,Deep transfer network | Journal | 169 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang Shi | 1 | 0 | 0.34 |
| Zhenyu Wu | 2 | 0 | 0.68 |
| Xiaomeng Lv | 3 | 0 | 0.34 |
| Ji Yang | 4 | 35 | 8.74 |